Llama-Nemotron-Post-Training-Dataset-v1|AI模型训练数据集|自然语言处理数据集

# Llama-Nemotron-Post-Training-Dataset-v1.1 Release

**Update [4/8/2025]:**

**v1.1:** We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of [Llama-3.1-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1). 🎉
## Data Overview

This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of [Llama-3.1-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1), [Llama-3.3-Nemotron-Super-49B-v1](https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1) and [Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1).

Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) which is a derivative of [Meta Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) (AKA the *reference model*).
Llama-3.3-Nemotron-Super-49B-v1 is an LLM which is a derivative of [Meta Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) (AKA the *reference model*). Llama-3.1-Nemotron-Nano-8B-v1 is an LLM which is a derivative of [Meta Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (AKA the *reference model*). They are aligned for human chat preferences, and tasks.

These models offer a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint and enable larger workloads. This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. The models support a context length of 128K.

This dataset release represents a significant move forward in openness and transparency in model development and improvement. By releasing the complete training set, in addition to the training technique, tools and final model weights, NVIDIA supports both the re-creation and the improvement of our approach.

## Data distribution

| Category | Value |
|----------|-----------|
| math | 22,066,397|
| code | 10,108,883 |
| science | 708,920 |
| instruction following | 56,339 |
| chat | 39,792 |
| safety | 31,426 |

## Filtering the data

Users can download subsets of the data based on the metadata schema described above. Example script for downloading code and math as follows:
```
from datasets import load_dataset
ds = load_dataset("nvidia/Llama-Nemotron-Post-Training-Dataset", "SFT", split=["code", "math"])
```

## Prompts

Prompts have been sourced from either public and open corpus or synthetically generated. All responses have been synthetically generated from public and open models.

The prompts were extracted, and then filtered for quality and complexity, or generated to meet quality and complexity requirements. This included filtration such as removing inconsistent prompts, prompts with answers that are easy to guess, and removing prompts with incorrect syntax.

## Responses

Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes.

Models that were used in the creation of this dataset:

| Model | Number of Samples |
|----------|-----------|
| Llama-3.3-70B-Instruct | 420,021 |
| Llama-3.1-Nemotron-70B-Instruct | 31,218 |
| Llama-3.3-Nemotron-70B-Feedback/Edit/Select | 22,644 |
| Mixtral-8x22B-Instruct-v0.1 | 31,426 |
| DeepSeek-R1 | 3,934,627 |
| Qwen-2.5-Math-7B-Instruct | 19,840,970 |
| Qwen-2.5-Coder-32B-Instruct | 8,917,167 |
| Qwen-2.5-72B-Instruct | 464,658 |
| Qwen-2.5-32B-Instruct | 2,297,175 |

## License/Terms of Use

The dataset contains information about license type on a per sample basis. The dataset is predominantly CC-BY-4.0, with a small subset of prompts from Wildchat having an ODC-BY license and a small subset of prompts from StackOverflow with CC-BY-SA license.

This dataset contains synthetic data created using Llama-3.3-70B-Instruct, Llama-3.1-Nemotron-70B-Instruct and
Llama-3.3-Nemotron-70B-Feedback/Edit/Select (ITS models). If this dataset is used to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, such AI model may be subject to redistribution and use requirements in the Llama 3.1 Community License Agreement and Llama 3.3 Community License Agreement.

**Data Developer:** NVIDIA

### Use Case:
Developers training AI Agent systems, chatbots, RAG systems, and other AI-powered applications.

### Release Date:
4/8/2025

## Data Version
1.1 (4/8/2025)

## Intended use

The Llama Nemotron Post-Training Dataset is intended to be used by the community to continue to improve open models. The data may be freely used to train and evaluate.

## Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

## Data Opt-Out:
NVIDIA has undertaken legal review to ensure there is no confidential, PII or copyright materials. If, when reviewing or using this dataset, you identify issues with the data itself, such as those listed above, please contact [e-mail].

## Citation
```
@misc{bercovich2025llamanemotronefficientreasoningmodels,
title={Llama-Nemotron: Efficient Reasoning Models},
author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk},
year={2025},
eprint={2505.00949},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.00949},
}
```